https://github.com/audy21/machine-learning-exploratory

A recent Machine Learning playground, to get a better knowledge and practices.
https://github.com/audy21/machine-learning-exploratory

matplotlib pandas python pytorch scikit-learn tensorflow

Last synced: 2 months ago
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A recent Machine Learning playground, to get a better knowledge and practices.

• Host: GitHub
• URL: https://github.com/audy21/machine-learning-exploratory
• Owner: audy21
• Created: 2025-04-01T07:12:03.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-30T02:24:40.000Z (about 1 year ago)
• Last Synced: 2025-08-02T13:25:12.091Z (11 months ago)
• Topics: matplotlib, pandas, python, pytorch, scikit-learn, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 56 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🚀 Machine Learning Exploratory Projects

## Tools Used

- Python

- Pandas

- NumPy

- Scikit-learn

- PyTorch

- Matplotlib

- Seaborn

- Jupyter Notebook

## Featured Projects

### [Building an E-Commerce Clothing Classifier Model](https://github.com/audy21/machine-learning-exploratory/blob/main/Building%20an%20E-Commerce%20Clothing%20Classifier%20Model/notebook.ipynb)

- **Objective**: Develop a machine learning model to classify clothing items based on their features.

- **Tools Used**: Scikit-learn, Pandas, Matplotlib.

- **Output/Findings**: Achieved high accuracy in classifying clothing items, providing insights into feature importance for classification.

### [Predicting Movie Rental Durations](https://github.com/audy21/machine-learning-exploratory/blob/main/Predicting%20Movie%20Rental%20Durations/notebook.ipynb)

- **Objective**: Predict the rental duration of movies based on customer and movie attributes.

- **Tools Used**: Scikit-learn, Pandas, Seaborn.

- **Output/Findings**: Built a regression model with good predictive performance, identifying key factors influencing rental durations.

### [Predicting Temperature in London](https://github.com/audy21/machine-learning-exploratory/blob/main/Predicting%20Temperature%20in%20London/notebook.ipynb)

- **Objective**: Forecast daily temperatures in London using historical weather data.

- **Tools Used**: Scikit-learn, Pandas, Matplotlib.

- **Output/Findings**: Developed a time-series model that accurately predicts temperature trends, aiding in weather forecasting.

### [Predicting Traffic Volume with PyTorch](https://github.com/audy21/machine-learning-exploratory/blob/main/Predicting%20Traffic%20Volume%20with%20PyTorch/notebook.ipynb)

- **Objective**: Predict traffic volume on roads using deep learning techniques.

- **Tools Used**: PyTorch, Pandas, NumPy.

- **Output/Findings**: Implemented a neural network model that effectively predicts traffic volume, highlighting the impact of time and weather on traffic.

### [Predictive Modeling for Agriculture](https://github.com/audy21/machine-learning-exploratory/blob/main/Predictive%20Modeling%20for%20Agriculture/notebook.ipynb)

- **Objective**: Build a predictive model to assist in agricultural decision-making.

- **Tools Used**: Scikit-learn, Pandas, Seaborn.

- **Output/Findings**: Created a model that predicts crop yields based on environmental and soil factors, providing valuable insights for farmers.

## Conclusion

This repository showcases diverse applications of data analysis and machine learning, demonstrating the power of these techniques in solving real-world problems across various domains.